Multi-dimensional surface electromyogram signal prosthetic hand control method based on principal component analysis

1. A multi-dimensional surface electromyogram signal prosthetic hand control method based on principal component analysis, comprising the following steps: (1) wearing an armlet provided with an array electromyography sensor to a front arm of a subject, and respectively wearing five finger joint attitude sensors at a distal phalanx of a thumb and at middle phalanxes of remaining fingers of the subject, wherein the array electromyography sensor is a 24-channel array electromyography sensor; (2) performing independent bending and stretching training on the five fingers of the subject, and meanwhile, collecting data of an array electromyography sensor and data of the finger joint attitude sensors; (3) decoupling electromyography sensing data by a principal component analysis to form a finger motion training set of the subject, wherein the finger motion training set is represented by a matrix, a number of rows of the matrix is a number of samples, a number of columns of the matrix is a number of channels of the array electromyography sensor, and original 24-dimensional data is reduced to 5-dimensional data by principal component analysis; and removing the sensors worn on the fingers after the training is finished; (4) performing data fitting on the finger motion training set by a neural network method, and constructing a finger continuous motion prediction model; and (5) predicting a current bending angle of the finger through the finger continuous motion model in the step (4).

2. The multi-dimensional surface electromyogram signal prosthetic hand control method according to claim 1 , wherein the independent bending and stretching training on the five fingers in the step (2) specifically comprises: repeatedly bending and stretching each finger for ten times, pausing for 30 seconds after completing one round of motions of the five fingers, then performing a second set of motions, two sets of motions being performed in total; collecting and preprocessing the electromyography signals in the training process, and stopping collecting during the pauses; and representing original electromyography data by a muscle activity.

3. The multi-dimensional surface electromyogram signal prosthetic hand control method according to claim 2 , wherein the preprocessing comprises representation and normalization processing on the muscle activity of the electromyography signals, and quaternion solution of attitude data.

4. The multi-dimensional surface electromyogram signal prosthetic hand control method according to claim 1 , wherein in the step (4), a three-layer neural network structure is used, five neurons are arranged in an input layer, 15 neurons are arranged in a hidden layer, and five neurons are arranged in an output layer; transmission functions of the hidden layer and the output layer of the neural network are a Sigmoid function and a linear function respectively; and the finger motion training set collected in the step (3) is used as a sample for error back propagation calculation to solve network parameters thereof.

5. The multi-dimensional surface electromyogram signal prosthetic hand control method according to claim 1 , wherein in the step (5), after the current bending angle of the finger is predicted, a bending angle variation of the finger is converted into an actual control amount of a motor, which specifically comprises the following steps of: (6.1) designing an underactuated control model for a prosthetic hand finger; (6.2) calculating a motion equation of an estimated bending angle of the finger and a rotation angle of a stepping motor by analyzing a motion trajectory of the prosthetic hand finger; (6.3) substituting the estimated bending angle of the finger into the motion equation in the step (6.2) to obtain an output rotation angle of the stepping motor; and (6.4) controlling the stepping motor to rotate at a corresponding angle through a microcontroller.